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预测非小细胞肺癌术后辅助化疗持久临床获益:基于 CT 影像与免疫分型的列线图

英文原题:Predicting Durable Clinical Benefits of Postoperative Adjuvant Chemotherapy in Non-small Cell Lung Cancer: A Nomogram Based on CT Imaging and Immune Type.

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Predicting Durable Clinical Benefits of Postoperative Adjuvant Chemotherapy in Non-small Cell Lung Cancer: A Nomogram Based on CT Imaging and Immune Type.

PubMed 2024/08/16(内容时间) Acad Radiol Q1 · IF 4.7(JCR 2025)

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研究概要

常规 CT 征象和 TIMT 为预测 NSCLC 患者术后辅助化疗的临床结局提供了一种有前景的方法。

研究思路结论见上方概要

构建基于常规CT征象和肿瘤微环境免疫分型(TIMT)的模型,以预测非小细胞肺癌(NSCLC)术后辅助化疗的持久临床获益(DCB)。

共205例NSCLC患者术前行CT检查,分为两组:DCB组(无进展生存期(PFS)≥18个月)和non-DCB组(NDCB,PFS <18个月)。对PD-L1和CD8 + TIL(肿瘤浸润淋巴细胞)的密度百分位数进行量化,以评估TIMT。收集临床特征和常规CT征象。采用多因素logistic回归筛选最具鉴别力的参数,构建预测模型,并将模型可视化为列线图。使用受试者工作特征(ROC)曲线、校准曲线和决策曲线分析(DCA)评估预测性能和临床实用性。

在NSCLC中, precisely 118例DCB患者和87例NDCB患者接受了术后辅助化疗。TIMT在DCB组和NDCB组之间差异有统计学意义(P < 0.05)。临床特征(神经元特异性烯醇化酶、鳞状细胞癌抗原、Ki-76和cM分期)和常规CT征象(毛刺征、空泡征、胸膜牵拉征、最大径和静脉期CT值)在四个TIMT组之间有所不同(P < 0.05)。此外,临床特征(淋巴细胞计数[LYMPH]和cM分期)和常规CT征象(空泡征和胸腔积液)在DCB组和NDCB组之间有所不同(P < 0.05)。多因素分析显示,TIMT、cM分期、LYMPH和胸腔积液与DCB独立相关,并用于构建列线图。联合模型的曲线下面积(AUC)为0.70(95%CI:0.64-0.76),灵敏度和特异度分别为0.73和0.60。

展开英文摘要原文

To develop a model based on conventional CT signs and the tumor microenvironment immune types (TIMT) to predict the durable clinical benefits (DCB) of postoperative adjuvant chemotherapy in non-small cell lung cancer (NSCLC). METHODS AND MATERIALS: A total of 205 patients with NSCLC underwent preoperative CT and were divided into two groups: DCB (progression-free survival (PFS) 18 months) and non-DCB (NDCB, PFS <18 months). The density percentiles of PD-L1 and CD8 + tumor-infiltrating lymphocytes (TIL) were quantified to estimate the TIMT. Clinical characteristics and conventional CT signs were collected. Multivariate logistic regression was employed to select the most discriminating parameters, construct a predictive model, and visualize the model as a nomogram. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to evaluate prediction performance and clinical utility.

Precisely 118 patients with DCB and 87 with NDCB in NSCLC received postoperative adjuvant chemotherapy. TIMT was statistically different between the DCB and NDCB groups (P < 0.05). Clinical characteristics (neuron-specific enolase, squamous cell carcinoma antigen, Ki-76, and cM stage) and conventional CT signs (spiculation, bubble-like lucency, pleural retraction, maximum diameter, and CT value of the venous phase) varied between the four TIMT groups (P < 0.05). Furthermore, clinical characteristics (lymphocyte count [LYMPH] and cM stage) and conventional CT signs (bubble-like lucency and Pleural effusion) differed between the DCB and NDCB groups (P < 0.05). Multivariate analysis revealed that TIMT, cM stage, LYMPH, and pleural effusion were independently associated with DCB and were used to construct a nomogram. The area under the curve (AUC) of the combined model was 0.70 (95%CI: 0.64-0.76), with sensitivity and specificity of 0.73 and 0.60, respectively.

Conventional CT signs and the TIMT offer a promising approach to predicting clinical outcomes for patients treated with postoperative adjuvant chemotherapy in NSCLC.

论文信息

作者
Deng L、Zhang M、Zhu K、Ren J、Zhang P、Zhang Y、Jing M、Han T
第一作者单位
Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou 730000, China; Second Clinical School, Lanzhou University, Lanzhou 730000, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730000, China.China
通讯作者单位
Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou 730000, China; Second Clinical School, Lanzhou University, Lanzhou 730000, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730000, China. Electronic address: ery_zhoujl@lzu.edu.cn.China
文献类型
非美国政府资助研究
期刊
Academic radiology2025 Jan
原文标识
PubMed 39153960 · DOI 10.1016/j.acra.2024.07.004